System and Method of Segmented Modelling for Campaign Planning in a Very Large-Scale Supply Chain Network

ABSTRACT

A system and method are disclosed including a planner having a processor and memory. The planner models a supply chain network over a planning horizon having one or more time buckets and a production line to produce one or more products using one or more campaign operations and one or more campaignable resources. The planner further formulates a supply chain master planning problem comprising a hierarchy of objective functions and one or more constraints and segments a campaign planning problem into three stages. The planner solves a first stage to determine prioritized production demands on campaignable buffers, solves a second stage to determine a timing and a sequence for allocating campaignable resources to campaignable buffers, and solves a third stage to determine a quantity of products to produce on campaignable resources.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/968,352, filed May 1, 2018, entitled “System and Method of SegmentedModelling for Campaign Planning in a Very Large-Scale Supply ChainNetwork.” U.S. patent application Ser. No. 15/968,352 is assigned to theassignee of the present application.

TECHNICAL FIELD

The present disclosure relates generally to supply chain planning andspecifically to a system and method of segmented campaign planning.

BACKGROUND

A supply chain for manufactured items typically involves the procurementof raw materials, transforming the raw materials into finished goods,and distributing the finished goods to warehouses, retailers, andcustomers. A supply chain planner determines the flow and distributionof items in the supply chain to meet a demand for the finished goods,while ensuring compliance with any other business objectives andconstraints. Determining the solution is complicated by the fact thatthere is often a trade-off between inventory and production changeover.For example, producing long runs of a particular product line maydecrease changeovers, but it may undesirably increase inventory levelsof finished goods or works in progress for a given time frame. In somecases, high inventory is undesirable, for example, because freshness orstorage space is a constraint. Further, as one line is produced on aresource for a longer time in order to reduce production changeover,another product, which may have a high demand and low inventory, cannotbe simultaneously produced on the same resource. Thus, there aretrade-offs between inventory and production changeover that must betaken into account. To determine the proper allocation of thesecampaignable resources, a supply chain planner may use one or morelinear programming (LP) or heuristics solvers to generate a solution.However, previous solutions require a large amount of computingresources, and, even then, require many days to run, are unstable, andfail quickly in the presence of imperfect data. This inability todetermine the proper allocation of campaignable resources quickly forlarge supply chains in undesirable.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be derived byreferring to the detailed description when considered in connection withthe following illustrative figures. In the figures, like referencenumbers refer to like elements or acts throughout the figures.

FIG. 1 illustrates a supply chain planner, according to an embodiment;

FIG. 2 illustrates the supply chain planner of FIG. 1 in greater detail,according to an embodiment;

FIG. 3 illustrates a supply chain network model, according to anembodiment;

FIG. 4 illustrates components of a supply chain problem, which aremodified during segmented campaign planning, according to an embodiment;

FIG. 5 illustrates an exemplary method of segmented campaign planning,according to an embodiment;

FIG. 6 illustrates segmented campaign planning stages according to anembodiment;

FIG. 7 illustrates a comparison of results for single stage campaignplanning and segmented campaign planning for a small production dataset;and

FIG. 8 illustrates a comparison of results for single stage campaignplanning and segmented campaign planning for a large production dataset.

DETAILED DESCRIPTION

Aspects and applications of the invention presented herein are describedbelow in the drawings and detailed description of the invention. Unlessspecifically noted, it is intended that the words and phrases in thespecification and the claims be given their plain, ordinary, andaccustomed meaning to those of ordinary skill in the applicable arts.

In the following description, and for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various aspects of the invention. It will beunderstood, however, by those skilled in the relevant arts, that thepresent invention may be practiced without these specific details. Inother instances, known structures and devices are shown or discussedmore generally in order to avoid obscuring the invention. In many cases,a description of the operation is sufficient to enable one to implementthe various forms of the invention, particularly when the operation isto be implemented in software. It should be noted that there are manydifferent and alternative configurations, devices and technologies towhich the disclosed inventions may be applied. The full scope of theinventions is not limited to the examples that are described below.

Supply chain networks for the manufacture of items often comprise one ormore resources that require a non-trivial amount of time to be retaskedfrom the production of a first item to the production of a second item.These may be termed campaignable resources. By way of example only andnot by way of limitation, campaignable resources include soft drinkbottling machinery (which bottles many flavors of drinks into variousbottle sizes), tire manufacturing equipment (which produces varioustypes and styles of tires), and toy manufacturing equipment (which usesinterchangeable molds to produce many types of toys). According toembodiments, planning the sequence and allocation of campaignableresources in a supply chain plan may be termed campaign planning.

Because campaignable resources often comprise lengthy changeovers thatare sequence-dependent, if they are not accounted for during planning,the plan becomes infeasible during scheduling. As a result, backlog orshortage is increased. On the other hand, if changeovers are accountedfor during master planning, it leads to discrete constraints, changingthe problem structure from a linear program (LP) to a mixed integerprogram (MIP) and (in some cases) to mixed integer nonlinear program(MINLP). Though there are methods for solving each of these problems ona small scale, there is no method for solving large scale campaignplanning problems efficiently. As an example only and not by way oflimitation, a linear programming-based solver, such as, for example,linear programming optimization planning (LPOPT) solver is not scalablefor large datasets, such as global supply chains having many productionplants and a large number of finished products. For these types ofsupply chains, a typical solution may require computing resources with250 GB or RAM and a solve time over 140 hours. Additionally, thesesolutions are often unstable and, combined with the long solve time,cannot be used for weekly planning.

As described in more detail below, embodiments of the current disclosurecomprise linear optimization supply chain planning with segmentedcampaign planning that uses less computing resources and more quicklyplans campaignable resources inside of a master planning process thannon-segmented campaign planning while, at the same time, generatingplans with equal quality for small datasets and business-validated planquality with a significant decrease in runtime for large datasets

FIG. 1 illustrates exemplary supply chain network 100 according to anembodiment. Supply chain network 100 comprises supply chain planner 110,one or more imaging 150, computer 160, network 170, and communicationlinks 180-190. Although a single supply chain planner 110, one or moreimaging devices 120, a single inventory system 130, a singletransportation network 140, one or more supply chain entities 150, asingle computer 160, and a single network 170, are shown and described,embodiments contemplate any number of supply chain planners, imagingdevices, inventory systems, transportation systems, supply chainentities, computers, or networks, according to particular needs.

In one embodiment, supply chain planner 110 provides for campaignplanning within master planning. Supply chain planner 110 solves a LPproblem using a segmented approach to campaign planning, such that, thesolution in a given circumstance is arrived at by solving threesegmented campaign planning problems to generate a solution much fasterthan a single optimization problem. In one embodiment, supply chainplanner 110 comprises server 112 and database 114. According toembodiments, server 112 comprises one or more modules that model asupply chain network, formulate a supply chain planning problem,identify campaignable resources, segment the supply chain network intoat least three stages, and formulate and solve a series of segmentedcampaign planning problems. In an embodiment, supply chain planner 110stores supply chain data of one or more supply chain entities 150 ofsupply chain network 100 in database 114.

As an example only and not by way of limitation, supply chain network100 may be a global tire manufacturer and distributor, such as a tiremanufacturer supply chain network 100 and may comprise a large number ofdifferent items, such as, for example, over 22,000 different items,(each represented by a different SKU) that can be produced at one ofover a dozen manufacturing plants (manufacturers 154) worldwide. Aglobal transportation network 140 may then transport the produced tiresto meet demand for customers, distribution centers, and stockinglocations all over the world. Determining the best supply chain plan fora global supply chain quickly becomes a very large problem requiringcapturing the global demand and ensuring all of these demands are met inan efficient manner, while respecting all constraints and objectives.

As an example only and not by way of limitation, one of the mostdifficult parts of supply chain planning in the tire industry iscampaign planning the sequence of tire production during the curingprocess. The curing process receives green tires from upstreammanufacturing facilities and places the green tires in SKU-specificmolds where the tires are molded and cured before distributiondownstream for further processing and distribution. Each mold comprisesa number of different components, such as, for example, a tread ring, abead ring top, a bead ring bottom, a sidewall top, a sidewall bottom andonly produces a limited number of different types of tires, but demandrequires producing many different sizes and types of tires to match theenormous variety of automobiles. Therefore, the tire molds must bechanged frequently during tire production. However, changing molds isnon-trivial—the changeover requires assembling five separate maincomponents and consumes a substantial amount of time, such as, forexample, four to five hours, and due to these constraints only a limitednumber of molds can be changed. The curing process may be optimizedusing campaign planning, but solving the campaign planning problem iscomplicated by the depth and breadth of supply chain network 100, thedifferent modes that the tires may be transported across the globe tomeet demand, and other factors described herein. For example, when usinga LPOPT solver, a solution for the tire manufacturer supply chainnetwork requires approximately 120-130 hours. As described in moredetail below, the one or more engines and solvers of supply chainplanner 110 may solve the same problem in approximately 25-26 hourswhile producing a solution with demand satisfaction the same orequivalent to the LPOPT solver.

By way of a further example only and not by way of limitation, in thecase of a beverage manufacturer and distributor, the beverage may bemanufactured in various containers (such as, for example, a bottle(glass, plastic, cardboard container, can, or the like). The containersmay be of different sizes, such as, for example, 330 ml, 500 ml, or 1000ml, and the changeover times may be sequence dependent and vary from afew hours to several days or longer. Additionally, the bottling ofdifferent flavors (such as, for example, cola, lemon, orange, and thelike) may also be sequence dependent (for example, in an embodiment mildflavor to strong flavor is a preferred sequence) and involves setuptimes, which are typically less than the earlier setup times. Moreover,bottling may comprise upstream constraints (such as, for example, syrupstorage tanks), which favor running the bottling line for at least oneshift. Other constraints (such as, for example, shelf life criteria,freshness, and the like), require that beverages not remain in thesupply chain for more than a predetermined amount of time, such as, forexample, a particular number of days, weeks, months, or other like timeperiods.

In one embodiment, supply chain network 100 considers various discreteconstraints, such as, for example, sequence-dependent setup times,lot-sizing, storage, and shelf-life constraints of one or more supplychain entities 150 when solving supply chain campaign planning problems.As described below in more detail, these various discrete constraints,such as, for example, sequence-dependent setup times, lot-sizing,storage, and shelf-life constraints may prevent one or more supply chainentities 150 from satisfying supply chain demand and may delay supplychain demand from being satisfied during a particular planning horizon.Although, an exemplary supply chain network 100 is described asassociated with a tire manufacturer supply chain network 100,embodiments contemplate a beverage bottling supply chain network, a toymanufacturing supply chain network, and/or another like supply chainnetwork for any industry that may solve supply chain campaign planningproblems of any number of supply chain entities 150, according toparticular needs.

One or more imaging devices 120 comprise one or more processors 122,memory 124, one or more sensors 126, and may include any suitable inputdevice, output device, fixed or removable computer-readable storagemedia, or the like. According to embodiments, one or more imagingdevices 120 comprise an electronic device that receives imaging datafrom one or more sensors 126 or from one or more databases in supplychain network 100. One or more sensors 126 of one or more imagingdevices 120 may comprise an imaging sensor, such as, a camera, scanner,electronic eye, photodiode, charged coupled device (CCD), or any otherelectronic component that detects visual characteristics (such as color,shape, size, fill level, or the like) of objects. One or more imagingdevices 120 may comprise, for example, a mobile handheld electronicdevice such as, for example, a smartphone, a tablet computer, a wirelesscommunication device, and/or one or more networked electronic devicesconfigured to image items using sensor 126 and transmit product imagesto one or more databases.

In addition, or as an alternative, one or more sensors 126 may comprisea radio receiver and/or transmitter configured to read an electronictag, such as, for example, a radio-frequency identification (RFID) tag.Each item may be represented in supply chain network 100 by anidentifier, including, for example, Stock-Keeping Unit (SKU), UniversalProduct Code (UPC), serial number, barcode, tag, RFID, or like objectsthat encode identifying information. One or more imaging devices 120 maygenerate a mapping of one or more items in the supply chain network 100by scanning an identifier or object associated with an item andidentifying the item based, at least in part, on the scan. This mayinclude, for example, a stationary scanner located at one or more supplychain entities 150 that scans items as the items pass near the scanner.As explained in more detail below, supply chain planner 110, one or moreimaging devices 120, inventory system 130, and transportation network140 may use the mapping of an item to locate the item in supply chainnetwork 100.

Additionally, one or more sensors 126 of one or more imaging devices 120may be located at one or more locations local to, or remote from, theone or more imaging devices 120, including, for example, one or moresensors 126 integrated into one or more imaging devices 120 or one ormore sensors 126 remotely located from, but communicatively coupledwith, one or more imaging devices 120. According to some embodiments,one or more sensors 126 may be configured to communicate directly orindirectly with one or more of supply chain planner 110, one or moreimaging devices 120, inventory system 130, transportation network 140,one or more supply chain entities 150, computer 160, and/or network 170using one or more communication links 180-190.

Inventory system 130 comprises server 132 and database 134. Server 132of inventory system 130 is configured to receive and transmit item data,including item identifiers, pricing data, attribute data, inventorylevels, and other like data about one or more items at one or morelocations in the supply chain network 100. Server 132 stores andretrieves item data from database 144 or from one or more locations insupply chain network 100.

Transportation network 140 comprises server 142 and database 144.According to embodiments, transportation network 140 directs one or moretransportation vehicles 146 to ship one or more items between one ormore supply chain entities 150, based, at least in part, on a supplychain plan, including a supply chain master plan and/or a campaign plan,the number of items currently in stock at one or more supply chainentities 150, the number of items currently in transit in thetransportation network 140, forecasted demand, a supply chaindisruption, and/or one or more other factors described herein.Transportation vehicles 146 comprise, for example, any number of trucks,cars, vans, boats, airplanes, unmanned aerial vehicles (UAVs), cranes,robotic machinery, or the like. Transportation vehicles 146 may compriseradio, satellite, or other communication that communicates locationinformation (such as, for example, geographic coordinates, distance froma location, global positioning satellite (GPS) information, or the like)with supply chain planner 110, one or more imaging devices 120,inventory system 130, transportation network 140, and/or one or moresupply chain entities 150 to identify the location of the transportationvehicle 146 and the location of any inventory or shipment located on thetransportation vehicle 146.

As shown in FIG. 1 , supply chain network 100 comprising supply chainplanner 110, one or more imaging devices 120, inventory system 130,transportation network 140, and one or more supply chain entities 150may operate on one or more computers 160 that are integral to orseparate from the hardware and/or software that support supply chainplanner 110, one or more imaging devices 120, inventory system 130,transportation network 140, and one or more supply chain entities 150.Computers 160 may include any suitable input device 162, such as akeypad, mouse, touch screen, microphone, or other device to inputinformation. Output device 164 may convey information associated withthe operation of supply chain network 100, including digital or analogdata, visual information, or audio information. Computer 160 may includefixed or removable computer-readable storage media, including anon-transitory computer readable medium, magnetic computer disks, flashdrives, CD-ROM, in-memory device or other suitable media to receiveoutput from and provide input to supply chain network 100.

Computer 160 may include one or more processors 166 and associatedmemory to execute instructions and manipulate information according tothe operation of supply chain network 100 and any of the methodsdescribed herein. In addition, or as an alternative, embodimentscontemplate executing the instructions on computer 160 that causecomputer 160 to perform functions of the method. Further examples mayalso include articles of manufacture including tangible non-transitorycomputer-readable media that have computer-readable instructions encodedthereon, and the instructions may comprise instructions to performfunctions of the methods described herein.

In addition, and as discussed herein, supply chain network 100 maycomprise a cloud-based computing system having processing and storagedevices at one or more locations, local to, or remote from supply chainplanner 110, one or more imaging devices 120, inventory system 130,transportation network 140, and one or more supply chain entities 150.In addition, each of the one or more computers 160 may be a workstation, personal computer (PC), network computer, notebook computer,tablet, personal digital assistant (PDA), cell phone, telephone,smartphone, wireless data port, augmented or virtual reality headset, orany other suitable computing device. In an embodiment, one or more usersmay be associated with the inventory planner 110, one or more imagingdevices 120, inventory system 130, transportation network 140, and oneor more supply chain entities 150. These one or more users may include,for example, a “manager” or a “planner” handling supply chain planning,campaign planning, and/or one or more related tasks within the system.In addition, or as an alternative, these one or more users within thesystem may include, for example, one or more computers programmed toautonomously handle, among other things, one or more supply chainprocesses such as demand planning, supply and distribution planning,inventory management, allocation planning, order fulfilment, adjustmentof manufacturing and inventory levels at various stocking points, and/orone or more related tasks within supply chain network 100.

One or more supply chain entities 150 represent one or more supply chainnetworks, including one or more enterprises, such as, for examplenetworks of one or more suppliers 152, manufacturers 154, distributioncenters 156, retailers 158 (including brick and mortar and onlinestores), customers, and/or the like. Suppliers 152 may be any suitableentity that offers to sell or otherwise provides one or more items(i.e., materials, components, or products) to one or more manufacturers154. Suppliers 152 may comprise automated distribution systems 153 thatautomatically transport products to one or more manufacturers 154 based,at least in part, on a supply chain plan, including a supply chainmaster plan and/or a campaign plan, the number of items currently instock at one or more supply chain entities 150, the number of itemscurrently in transit in the transportation network 140, forecasteddemand, a supply chain disruption, and/or one or more other factorsdescribed herein.

Manufacturers 154 may be any suitable entity that manufactures at leastone product. Manufacturers 154 may use one or more items during themanufacturing process to produce any manufactured, fabricated,assembled, or otherwise processed item, material, component, good, orproduct. In one embodiment, a product represents an item ready to besupplied to, for example, one or more supply chain entities 150 insupply chain network 100, such as retailers 158, an item that needsfurther processing, or any other item. Manufacturers 154 may, forexample, produce and sell a product to suppliers 152, othermanufacturers 154, distribution centers 156, retailers 158, a customer,or any other suitable person or entity. Manufacturers 154 may compriseautomated robotic production machinery 155 that produce products based,at least in part, on a supply chain plan, including a supply chainmaster plan and/or a campaign plan, the number of items currently instock at one or more supply chain entities 150, the number of itemscurrently in transit in the transportation network 140, forecasteddemand, a supply chain disruption, and/or one or more other factorsdescribed herein.

Distribution centers 156 may be any suitable entity that offers to storeor otherwise distribute at least one product to one or more retailers158 and/or customers. Distribution centers 156 may, for example, receivea product from a first one or more supply chain entities 150 in supplychain network 100 and store and transport the product for a second oneor more supply chain entities 150. Distribution centers 156 may compriseautomated warehousing systems 157 that automatically remove productsfrom and place products into inventory based, at least in part, on asupply chain plan, including a supply chain master plan and/or acampaign plan, the number of items currently in stock at one or moresupply chain entities 150, the number of items currently in transit inthe transportation network 140, forecasted demand, a supply chaindisruption, and/or one or more other factors described herein.

Retailers 158 may be any suitable entity that obtains one or moreproducts to sell to one or more customers. Retailers 158 may compriseany online or brick-and-mortar store, including stores with shelvingsystems 159. Shelving systems may comprise, for example, various racks,fixtures, brackets, notches, grooves, slots, or other attachment devicesfor fixing shelves in various configurations. These configurations maycomprise shelving with adjustable lengths, heights, and otherarrangements, which may be adjusted by an employee of retailers 158based on computer-generated instructions or automatically by machineryto place products in a desired location in retailers 158 and which maybe based, at least in part, on a supply chain plan, including a supplychain master plan and/or a campaign plan, the number of items currentlyin stock at one or more supply chain entities 150, the number of itemscurrently in transit in the transportation network 140, forecasteddemand, a supply chain disruption, and/or one or more other factorsdescribed herein.

Although one or more supply chain entities 150 are shown and describedas separate and distinct entities, the same entity may simultaneouslyact as any one of the one or more supply chain entities 150. Forexample, one or more supply chain entities 150 acting as a manufacturercan produce a product, and the same one or more supply chain entities150 can act as a supplier to supply an item to itself or another one ormore supply chain entities 150. Although one example of a supply chainnetwork 100 is shown and described, embodiments contemplate anyconfiguration of supply chain network 100, without departing from thescope described herein.

In one embodiment, supply chain planner 110 may be coupled with network170 using communications link 180, which may be any wireline, wireless,or other link suitable to support data communications between supplychain planner 110 and network 170 during operation of supply chainnetwork 100. One or more imaging devices 120 may be coupled with network170 using communications link 182, which may be any wireline, wireless,or other link suitable to support data communications between one ormore imaging devices 120 and network 170 during operation of supplychain network 100. Inventory system 130 may be coupled with network 170using communications link 184, which may be any wireline, wireless, orother link suitable to support data communications between inventorysystem 130 and network 170 during operation of supply chain network 100.Transportation network 140 may be coupled with network 170 usingcommunications link 186, which may be any wireline, wireless, or otherlink suitable to support data communications between transportationnetwork 140 and network 170 during operation of supply chain network100. One or more supply chain entities 150 may be coupled with network170 using communications link 188, which may be any wireline, wireless,or other link suitable to support data communications between one ormore supply chain entities 150 and network 170 during operation ofsupply chain network 100. Computer 160 may be coupled with network 170using communications link 190, which may be any wireline, wireless, orother link suitable to support data communications between computer 160and network 170 during operation of supply chain network 100.

Although communication links 180-190 are shown as generally couplingsupply chain planner 110, one or more imaging devices 120, inventorysystem 130, transportation network 140, one or more supply chainentities 150, and computer 160 to network 170, any of supply chainplanner 110, one or more imaging devices 120, inventory system 130,transportation network 140, one or more supply chain entities 150, andcomputer 160 may communicate directly with each other, according toparticular needs.

In another embodiment, network 170 includes the Internet and anyappropriate local area networks (LANs), metropolitan area networks(MANs), or wide area networks (WANs) coupling supply chain planner 110,one or more imaging devices 120, inventory system 130, transportationnetwork 140, one or more supply chain entities 150, and computer 160.For example, data may be maintained locally to, or externally of, supplychain planner 110, one or more imaging devices 120, inventory system130, transportation network 140, one or more supply chain entities 150,and computer 160 and made available to one or more associated users ofsupply chain planner 110, one or more imaging devices 120, inventorysystem 130, transportation network 140, one or more supply chainentities 150, and computer 160 using network 170 or in any otherappropriate manner. For example, data may be maintained in a clouddatabase at one or more locations external to supply chain planner 110,one or more imaging devices 120, inventory system 130, transportationnetwork 140, one or more supply chain entities 150, and computer 160 andmade available to one or more associated users of supply chain planner110, one or more imaging 150, and computer 160 using the cloud or in anyother appropriate manner. Those skilled in the art will recognize thatthe complete structure and operation of network 170 and other componentswithin supply chain network 100 are not depicted or described.Embodiments may be employed in conjunction with known communicationsnetworks and other components.

In accordance with the principles of embodiments described herein,supply chain planner 110 may generate a supply chain plan, including asupply chain master plan and/or a campaign plan. Furthermore, supplychain planner 110 may instruct automated machinery (i.e., roboticwarehouse systems, robotic inventory systems, automated guided vehicles,mobile racking units, automated robotic production machinery, roboticdevices and the like) to adjust product mix ratios, inventory levels atvarious stocking points, production of products of manufacturingequipment, proportional or alternative sourcing of one or more supplychain entities, and the configuration and quantity of packaging andshipping of items based on a supply chain plan, including a supply chainmaster plan and/or a campaign plan, the number of items currently in theinventory one or more supply chain entities 150, the number of itemscurrently in transit in the transportation network 140, forecasteddemand, a supply chain disruption, and/or one or more other factorsdescribed herein. For example, the methods described herein may includecomputers 160 receiving product data 212 (FIG. 2 ) from automatedmachinery having at least one sensor and product data 212 correspondingto an item detected by the automated machinery. The received productdata 212 may include an image of the item, an identifier (such as aSKU), as described above, and/or other product information associatedwith the item (dimensions, texture, estimated weight, fill level, andthe like). The method may further include computers 160 looking up thereceived product data 212 in a database system associated with supplychain planner 110 to identify the item corresponding to the product data212 received from the automated machinery.

Computers 170 may also receive, from one or more sensors 126 of the oneor more imaging devices, a current location of the identified item.Based on the identification of the item, computers 160 may also identify(or alternatively generate) a first mapping in the database system,where the first mapping is associated with the current location of theidentified item. Computers 160 may also identify a second mapping in thedatabase system, where the second mapping is associated with a pastlocation of the identified item. Computers 160 may also compare thefirst mapping and the second mapping to determine if the currentlocation of the identified item in the first mapping is different thanthe past location of the identified item in the second mapping.Computers 160 may then send instructions to the automated machinerybased, as least in part, on one or more differences between the firstmapping and the second mapping such as, for example, to locate items toadd to or remove from an inventory of or package for one or more supplychain entities 150.

FIG. 2 illustrates supply chain planner 110 of FIG. 1 in greater detail,according to an embodiment. As discussed above, supply chain planner 110may comprise server 112 and database 114. Although supply chain planner110 is shown as comprising a single server 112 and a single database114, embodiments contemplate any suitable number of servers or databasesinternal to or externally coupled with supply chain planner 110.

Server 112 of supply chain planner 110 may comprise one or more enginesor solvers 200 for generating an optimized solution and/or plan ofsupply chain campaign planning problems of supply chain network 100. Theone or more engines or solvers 200 may solve the segmented campaignplanning problems of a supply chain master planning problem using theresults of a previous segmented campaign planning problem and datamanipulations performed between planning problems. In an embodiment,supply chain planner 110 stores and retrieves supply chain masterplanning problem data, such as, for example, Linear Programming (LP)optimized plans of supply chain network 100 in database 114. Inaddition, although server 112 is shown and described as comprising oneor more engines or solvers 200, embodiments contemplate any suitablenumber or combination of engines and/or solvers, according to particularneeds.

As discussed in more detail below, solver 200 models one or more supplychain master planning problems of supply chain network 100. That is,solver 200 of server 112 models the one or more supply chain masterplanning problems of one or more supply chain entities 150 to representthe flow of materials through supply chain network 100. In addition,supply chain network 100, including the one or more supply chain masterplanning problems, is valid for a particular period of interest, i.e., aplanning horizon.

Database 114 comprises one or more databases or other data storagearrangements at one or more locations, local to, or remote from, server112. Database 114 includes, for example, supply chain data 210, productdata 212, inventory data 214, demand data 216, and data models 218.Although, database 114 is shown and described as comprising supply chaindata 210, product data 212, inventory data 214, demand data 216, anddata models 218, embodiments contemplate any suitable arrangement orcombination of data storage, located at one or more locations, local to,or remote from, supply chain planner 110, according to particular needs.

As an example only and not by way of limitation, database 114 storessupply chain data 210, including one or more supply chain masterplanning problems of supply chain network 100 that may be used by server112, and/or solver 200. Supply chain data 210 may comprise for example,various decision variables, business constraints, goals and objectivesof one or more supply chain entities 150. According to some embodiments,supply chain data 210 may comprise hierarchical objectives specified by,for example, business rules, campaign data, master planning requirementsalong with scheduling constraints and discrete constraints, such as, forexample, sequence dependent setup times, lot-sizing, storage, shelflife, and other like constraints.

Product data 212 of database 114 may comprise one or more datastructures for identifying, classifying, and storing data associatedwith products, including, for example, a product identifier (such as aStock Keeping Unit (SKU), Universal Product Code (UPC) or the like),product attributes and attribute values, sourcing information, and thelike. Product data 210 may comprise data about one or more productsorganized and sortable by, for example, product attributes, attributevalues, product identification, sales quantity, demand forecast, or anystored category or dimension. Attributes of one or more products may be,for example, any categorical characteristic or quality of a product, andan attribute value may be a specific value or identity for the one ormore products according to the categorical characteristic or quality,including, for example, physical parameters (such as, for example, size,weight, dimensions, fill level, color, and the like).

Inventory data 214 of database 114 may comprise any data relating tocurrent or projected inventory quantities or states. According toembodiments, inventory data 214 may comprise the current level ofinventory for items at one or more stocking points across supply chainnetwork 100. In addition, inventory data 214 may comprise order rulesthat describe one or more rules or limits on setting an inventorypolicy, including, but not limited to, a minimum order quantity, amaximum order quantity, a discount, a step-size order quantity, andbatch quantity rules. According to some embodiments, supply chainplanner 110 accesses and stores inventory data 214 in database 114,which may be used by supply chain planner 110 to place orders, setinventory levels at one or more stocking points, initiate manufacturingof one or more components, or the like. In addition, or as analternative, inventory data 214 may be updated by receiving current itemquantities, mappings, or locations from one or more imaging devices 120,inventory system 130, transportation system 140, and/or one or moresupply chain entities 150.

Demand data 216 of database 114 may comprise, for example, any datarelating to past sales, past demand, and purchase data of one or moresupply chain entities 150. Demand data 216 may be stored at timeintervals such as, for example, by the minute, hour, daily, weekly,monthly, quarterly, yearly, or any suitable time interval, includingsubstantially in real time. According to embodiments, demand data 216may include historical demand or projected demand forecasts for one ormore retail locations, customers, regions, or the like of one or moresupply chain entities 150 and may include product attribute demand orforecasts.

Data models 218 represent the flow of materials through one or moresupply chain entities 150 of supply chain network 100 for one or moresupply chain master planning problems having at least one campaignableresource. Solver 200 may model the flow of materials through one or moresupply chain entities 150 of supply chain network 100 as data models 218comprising a network of nodes and edges. The material storage and/ortransition units are modeled as nodes, which may be referred to as, forexample, buffer nodes, buffers, or nodes. Each node may represent abuffer for an item (such as, for example, a raw material, intermediategood, finished good, component, and the like), resource, or operation(including, for example, a production operation, assembly operation,transportation operation, and the like). Various transportation ormanufacturing processes are modeled as edges connecting the nodes. Eachedge may represent the flow, transportation, or assembly of materials(such as items or resources) between the nodes by, for example,production processing or transportation. A planning horizon for the datamodels 218 may be broken down into elementary time-units, such as, forexample, time-buckets, or, simply, buckets. The edge between two buffernodes denote processing of material and the edge between differentbuckets for the same buffer indicates inventory carried forward.Flow-balance constraints for most, if not every buffer in every bucket,model the material movement in supply chain network 100.

FIG. 3 comprises an exemplary supply chain network model 300, accordingto an embodiment. In the exemplary supply chain network model 300,materials flow from upstream nodes to downstream nodes along each of theedges from left to right from raw materials to finished products.However, flows may be bidirectional, and one or more materials may flowfrom right to left, from a downstream node to an upstream node. Supplychain network represented by supply chain network model 300 begins atthe most upstream nodes representing raw material buffers 302 a-302 c.Raw material buffers 302 a-302 c may receive the initial input for amanufacturing process. For example, in a tire manufacturer supply chainnetwork, raw materials may comprise rubber, metal and fabric layers,adhesives, polymers, and other materials and compounds required for tiremanufacturing. The flow of raw materials is indicated by edges 304 a-304e, which identify which of upstream production processes 306 a-306 c isa possible destination for raw materials from raw material buffers 302a-302 c. For example, raw materials from first raw material buffer 302 amay be transported to either first upstream production process 306 a orsecond upstream production process 306 b as indicated by edges 304 a-304b. In contrast, raw material from second raw material buffer 302 b maybe transported only to second upstream production process 306 b asindicated by edge 304 c. Raw materials from raw material buffer 302 cmay be transported to either second upstream production process 306 b orthird upstream production process 306 c as indicated by edges 304 d-304e. The results of processing the raw materials transported to upstreamproduction processes 306 a-306 c is indicated by edges 308 a-308 e.Edges 308 a-308 e indicate that raw material transported from rawmaterial buffers 302 a-302 c to each of upstream production processes306 a-306 c is transformed into upstream intermediate items stored atupstream intermediate items buffers 310 a-310 d. Continuing with theexemplary tire manufacturer supply chain network example, these upstreamintermediate items may represent different types of ‘green tires.’ Greentires comprise the blank tires which are placed into molds to cure andform a tread pattern determined by the mold.

As indicated by edges 308 a-308 e, each of the upstream productionprocesses 306 a-306 c supplies upstream intermediate items to onlyparticular upstream intermediate item buffers 310 a-310 d. For example,first upstream production process 306 a supplies only first upstreamintermediate item buffer 310 a (indicated by edge 308 a), while secondupstream production process 306 b may supply either first upstreamintermediate item buffer 310 a or second upstream intermediate itembuffer 310 b (indicated by edges 308 b-308 c). Neither first upstreamproduction process 306 a nor second upstream production process 306 bmay supply third upstream intermediate item buffer 310 c or fourthupstream intermediate item buffer 310 d (indicated by no edge connectingthese buffers). Instead, third upstream production process 306 c maysupply either third upstream intermediate item buffer 310 c or fourthupstream intermediate item buffer 310 d (indicated by edges 308 d-308e), but not first upstream intermediate item buffer 310 a or secondupstream intermediate item buffer 310 b (indicated by no edge connectingthese buffers).

These limitations on supplying upstream intermediate items to particularbuffers may represent transportation limitations between upstreamproduction processes 306 a-306 c and intermediate item buffers 310 a-310d. For example, first and second upstream production processes 306 a-306b may be located in a first country, such as the United States, whilethird upstream production process 306 c may be located in a secondcountry, such as Spain. If first and second upstream intermediate itembuffers 310 a-310 b are located in the United States and Canada,respectively, and third and fourth upstream intermediate item buffers310 c-310 d are located in Spain and Germany, then the cost, time,available transportation options, or the like may limit the flow ofitems between one or more buffers of supply chain network model 300.Additionally, flow between the nodes may be limited by what items areproduced from each of production processes 306 a-306 c. For example,production processes 306 a-306 c may comprise different productionprocesses, which produce different items, each of which may berepresented by a different SKU, and which each may be stored atdifferent upstream intermediate item buffers 310 a-310 d. Although thelimitation of the flow of items between nodes of supply chain networkmodel 300 is described as cost, timing, transportation, or productionlimitations, embodiments contemplate any suitable flow of items orlimitations of the flow of items between any one or more different nodesof a supply chain network, according to particular needs.

Next, the upstream intermediate items from upstream intermediate itembuffers 310 a-310 d are further transported, as indicated by edges 312a-312 i, to campaignable operations 314 a-314 c. Campaign operations 314a-314 c are coupled by edges 316 a-316 c with campaignable resource 318to indicate that campaignable operations 314 a-314 c requirecampaignable resource 318 in order to process the upstream intermediateitems. According to embodiments, campaignable resource 318 comprises anyresource which requires a significant amount of time to change fromproduction of a first item to production of a second item (i.e. achangeover time). Campaignable resource 318 may include, for example,particular manufacturing, distribution, or transportation equipment andfacilities, and other such resources utilized in the supply chain.

Continuing with the exemplary tire manufacturer supply chain networkexample, campaignable resource 318 may comprise the tire manufacturingequipment that is required for curing tires, but which may produce onlyone tire SKU at a time and which requires a significant change over timeto switch between production of a first tire SKU to a second tire SKU.Therefore, solver 200 will use campaign planning to determine the orderand length of time each campaignable operations 314 a-314 c should usecampaignable resource 318. As described below, solver 200 will perform acampaign planning process to determine the allocation of thecampaignable resource 318 based, in part, on upstream demands andcapacity and material constraints. As indicated by edges 320 a-320 c,campaignable operations 314 a-314 c produce campaign goods stored atcampaignable buffers 322 a-322 b, which, continuing with the example ofthe tire manufacturer supply chain network, may comprise cured tireswhich are ready for final processing before shipment to one or morecustomers or supply chain entities 150. For example, campaign goods fromcampaignable buffers 322 a-322 b may, as indicated by edges 324 a-324 d,be transported to final production processes 326 a-326 c. Finalproduction processes 326 a-336 c transform the campaign goods asindicated by edges 328 a-328 d into finished goods stored at finishedgoods buffers 330 a-330 c. Final production processes 326 a-326 c maycomprise one or more operations for finishing, testing, packaging,transportation, and the like of the campaign goods to produce finishedgoods held at finished goods buffers 330 a-330 c. Finished goods buffers330 a-330 c comprise products ready to be packaged or transported to oneor more customers, distribution centers, or stocking locations in supplychain network 100. For example, for the exemplary tire manufacturer,final production processes 326 a-326 c may comprise inspection,measurement, or testing of cured tires for compliance with tolerances orsafety requirements. If the cured tires are compliant they may be markedfor sale and transported to finished goods buffers 330 a-330 c fordistribution to one or more customers or supply chain entities 150.

As indicated by edges 332 a-332 c, finished goods from finished goodsbuffers 330 a-330 c may be transported by transportation processes 334a-334 c for distribution to satisfy demands 336 a-336 c of one or morecustomers and/or one or more supply chain entities 150. For theexemplary tire manufacturer supply chain network, transportationprocesses 334 a-334 c transport, package, or ship finished to one ormore locations internal to or external of one or more supply chainentities 150 of supply chain network 100, including, for example,shipping tires directly to consumers, to regional or strategicdistribution centers, or to the inventory of one or more supply chainentities 150, including, for example, to replenish a safety stock forone or more tires in an inventory of one or more supply chain entities150. Although particular item and process described herein is asimplified description for the purpose of illustration. For example, theitems may be different sizes, styles, states of same or differentphysical material. Similarly, a process may be any process or operation,including manufacturing, distribution, transportation, or any othersuitable action of supply chain network 100. In one embodiment,additional constraints, such as, for example, business constraints,operation constraints, and resource constraints, may be added tofacilitate other planning rules.

Although, a simplified supply chain network model 300 is shown anddescribed as having a particular number of buffers, resources, andoperations with a defined flow between them, embodiments contemplate anynumber of buffers, resources, and operations with any suitable flowbetween them, including any number of nodes and edges, according toparticular needs. In particular, a supply chain master planning problemtypically comprises a supply chain network much more complex than thesimplified exemplary supply chain network model 300 described above. Forexample, a supply chain network often comprises multiple manufacturingplants located in different regions or countries. In addition, an itemmay be processed by many operations into a large number of differentintermediate goods and/or finished items, where the different operationsmay have multiple constrained resources and multiple input items, eachwith their own lead, transportation, production, and cycle times.Additionally, material may flow bidirectionally (either, upstream ordownstream), which is difficult to solve for heuristic solvers, but may,in some cases, be more quickly solved using LPOPT.

Supply chain network model 300 is used to generate a supply chainplanning problem. As described in more detail below, during segmentedsupply chain planning, solver 200 generates three supply chain planningproblems by selectively modifying different components of the supplychain planning problem.

FIG. 4 illustrates components of a supply chain problem, which aremodified during segmented campaign planning. A supply chain problemcomprises segmented supply chain network model 400, campaign objectives410, non-campaign objectives 412, and planning horizon 420. Segmentedsupply chain network model 400 comprises three segments 402-406.According to embodiments, segmented supply chain model 400 comprisesupstream segment 402, campaign segment 404, and downstream segment 406.Upstream segment 402 comprises all buffers and processes upstream ofcampaignable operations 314 a-314 c, including raw material buffers 302a-302 c, upstream production processes 306 a-306 c, upstreamintermediate items buffers 310 a-310 d, and edges 304 a-304 e and 308a-308 e. Campaign segment 404 comprises campaignable operations 314a-314 c, campaignable resource 318, campaignable buffers 322 a-322 b,and edges 316 a-316 c and 320 a-320 c. As described above, campaignableoperations 314 a-314 c (comprising, for example, a manufacturingprocess) require campaignable resource 318 (as indicated by edges 316a-316 c) to produce campaign goods stored at campaignable buffers 322a-322 b (as indicated by edges 320 a-320 c). Downstream segment 406comprises all buffers and processes after campaignable buffers 322 a-322b, including final production processes 326 a-326 c, finished goodsbuffers 330 a-330 c, transportation processes 334 a-334 c, demands 336a-336 c, and edges 328 a-328 d and 332 a-332 c. For example, continuingwith the exemplary tire manufacturer, upstream segment 402 comprises theportion of the supply chain responsible for green tire production.Campaign segment 404 comprises the curing process where the green tiresare placed in molds to form the particular shape and tread of aparticular tire represented by a particular SKU. Downstream segment 406comprises compliance and safety testing, packaging, and shipment to oneor more customer or supply chain entities 150 according to demands 336a-336 c.

Like supply chain planning, campaign planning comprises modeling one ormore objective functions (campaign objectives 410) and constraints(campaign constraints) and then solving the one or more campaignobjectives 410, hierarchically, according to the one or more campaignconstraints. Campaign constraints may comprise, for example, a maximumand/or minimum number of SKUs being produced a maximum and/or minimumnumber of changes from a previous bucket (i.e. minimize/maximizechangeovers), minimum runtime, and the like, while respecting a set ofhierarchical objectives. As described in more detail below, segmentedcampaign planning distinguishes between campaign objectives 410 andnon-campaign objectives 412. Campaign objectives 410 are guidingobjectives that are desired to be optimized while determining thecampaign sequence. As an example only and not by way of limitation,campaign objectives 410 may comprise demand satisfaction, just-in-timeproduction, alternate source minimization, and/or inventory reduction.In addition, non-campaign objectives 412 may comprise campaignobjectives which are important, but which the campaign sequence isallowed to violate. As an example only and not by way of limitation,non-campaign objectives 412 may comprise outsourcing minimization,production smoothing, and the like.

Supply chain planning is performed over a time period divided into oneor more time buckets. Planning horizon 420 comprises daily buckets 422,weekly buckets 424, and larger buckets 426. Larger buckets 426 maycomprise, for example, monthly, quarterly, or any other bucket sizelarger than weekly buckets, including aggregated buckets formed fromcombining one or more buckets into larger buckets. Additionally,although buckets are described as comprising daily buckets 422, weeklybuckets 424, and larger buckets 426, embodiments contemplate a planninghorizon formed from any combination of one or more buckets of anysuitable size. Continuing with the exemplary tire manufacturer, planninghorizon 420 may comprise one year for supply chain planning. To generateaccurate planning in the near term while speeding calculation of longterm planning, daily buckets 422 may be used for the first one, two,three, or more weeks, followed by weekly buckets 424 for the near- tomid-term (such as, for example, the next one to three months followingthe daily buckets 422), and larger 426 buckets (such as monthly buckets)for the remainder of the planning horizon 420.

FIG. 5 illustrates an exemplary method 500 of segmented campaignplanning, according to an embodiment. Segmented campaign planningcomprises creating and solving a multi-objective hierarchical linearoptimization using a segmented campaign planning process to quickly andoptimally plan campaign planning objectives. According to embodiments,method 500 eliminates the problem of prohibitive runtimes in thepresence of complex supply chains with fine-grained planning buckets,without diminishing the quality of the supply chain plan. Instead,method 500 may generate a near globally-optimal solution to a complexlarge-scale supply chain in presence of single-level campaign planningconstructs.

As described below, method 500 formulates and solves a supply chainproblem by segmenting the supply chain problem and solving threemodified supply chain planning problems.

FIG. 6 illustrates segmented campaign planning stages 602-606, accordingto an embodiment. According to embodiments, segmented campaign planningcomprises stage one 602, stage two 604, and stage three 604. Each stage602-604 comprises a supply chain problem having a different combinationof time-bucket granularity, supply chain static structure, andobjectives. The solution from each of stages 602-606 is used to generatean output which is passed sequentially, from one stage to the next, togenerate a near-optimal supply chain plan and campaign plan whilesimultaneously requiring a significantly smaller run time than anapproach comprising a single globally-optimal LPOPT run with campaignplanning. Each of three stages 602-606 may be run entirely in memorywithout exporting intermediate output to a database, which improvesperformance of method 500 of segmented campaign planning process.

Method 500 proceeds by one or more activities organized into threestages 602-606, which although described in a particular order andassociated with a particular stage 602-606, may be performed in one ormore permutations associated with or without any of stage 602-606,according to particular needs.

Method 500 begins at activity 510 of stage one 602, where solver 200models supply chain network 100 based on the static structure of thesupply chain. Supply chain network 100 may comprise an initial staticstructure, received by solver 200. According to embodiments, solver 200keeps the overall static structure intact when modeling supply chainnetwork 100 during stage one 602. However, as described in more detailbelow, the static structure of the supply chain represented by thesupply chain network model is modified during stage two 604 and revertedback to the initial static structure during stage three 606.

At action 512 of stage one 602, solver 200 converts daily buckets toweekly buckets. As discussed above, supply chain planning horizon 520comprises a bucketization scheme having daily buckets 422, weeklybuckets 424, monthly buckets, and/or one or more larger buckets 426. Forexample, planning horizon 420 may comprise daily buckets 422 fornear-term planning periods (up to, for example, about 40 days), followedby weekly buckets 424 for mid-term planning, then larger buckets 426(such as, monthly, quarterly, or aggregated buckets) through the end ofthe planning period. According to embodiments, any daily buckets 422used in planning prior to campaign segment 404 are converted into weeklybuckets 424. Any weekly buckets 424 are kept as weekly buckets 424,while monthly buckets may be limited in quantity to a preselectedparameter, such as, for example 9 months and any remaining buckets maybe aggregated into a single larger bucket 426 comprising an aggregatedbucket.

At action 514 of stage one 602, solver 200 formulates the supply chainproblem and solves the LPOPT objective followed by lotsizing withoutcampaign planning. Using the weekly buckets 424 and larger buckets 426,solver 200 removes campaignable objectives 410, and solves for demandsusing only non-campaign objectives 512. According to embodiments,lotsizing options are provided in an LPOPT option file for lot sizeplanning. In addition, or as an alternative, converting the dailybuckets to weekly buckets reduces the overall complexity of the supplychain solve due to the reduced number of buckets and providing moreflexibility to the solver 200 to utilize material and capacity at thecampaign stage.

At action 516 of stage one 602, solver 200 generates output from stageone 602 comprising a log file having prioritized production demand oncampaignable buffers in the temporary model. Before importing the log insolver 200, the log is stored in the temporary model. According toembodiments, the output of stage one 602 may be stored in a temporaryuser-defined model to improve performance. The log comprises informationabout partitions of each operation plan so that each part may beassigned to a specific demand to preserve the demand priority on thecampaignable operation plans.

Stage two 604 of method 500 begins at action 518, where solver 200modifies the static structure of the supply chain model. Based on theoutput from stage one 602, solver 200 generates a prioritized productionrequirement for campaign segment 404. According to embodiments, solver200 creates additional demands (dummy demands) on campaignable buffers314 a-314 c. While production plans were created in weekly buckets instage one 602, corresponding dummy demands are placed on the last dailybucket corresponding to the week, in stage two 604. Placing the demandon the last day of the week allows for even distribution of demandsacross the week, as solver 200 can always plan the production earlierbased on material availability. Further, solver 200 temporarilyeliminates all buffers, flows, and operations downstream of campaignablebuffers 314 a-314 c by marking each as lp_pass=false to indicatelike-wise to the solver 200. Additionally, the actual item requests forFinished Goods are marked as lp_pass=false so that the item requests arenot considered for planning in LPOPT. Further, any WIP/fixed_flows thatare defined downstream of campaignable buffers 314 a-314 c are alsomarked as lp_pass=false.

At activity 520 of stage two 604, solver 200 replaces daily buckets 422and aggregate buckets after weekly buckets 424. Additionally, solver 200manipulates the time bucket structure to enable near term daily buckets422 which are relevant to the campaign resource while also creating oneor more larger buckets 426 comprising aggregated buckets to generatesufficient demand for campaign planning. For example, according toembodiments, frozen daily buckets 422 are followed by campaignable dailybuckets 422. Weekly buckets 424 are added to cover the demands oncampaignable buffers 314 a-314 c calculated during stage one 602. Theremaining buckets are aggregated into one larger bucket 426 comprisingan aggregated bucket.

At activity 522 of stage two 606, solver 200 formulates the supply chainproblem and solves campaign objective iteratively to decide campaignsand followed by lotsizing. For example, solver 200 selectively enablesthe campaign planning objectives 510 (which were disabled at the firstplanning stage) and performs campaign planning on daily buckets usingLPOPT by calculating campaign planning for campaignable buffers 314a-314 c using breakout information from first stage campaign planningproblem and without using information from downstream segment 406 of thesupply chain plan. By eliminating downstream segment 406 from the secondcampaign planning problem, solver 200 may now use any LPOPT options forcampaign planning, with the exception of regular lotsizing options.

At activity 524 of stage two 606, solver 200 generates the output fromstage two as a campaign plan at the producing operations of campaignablebuffers 314 a-314 c.

Stage three 606 of method 500 begins at action 526, where solver 200reverts the supply chain static structure of the supply chain model backto the original state. For example, downstream segment 406 of supplychain network 100 is re-enabled by setting to lp_pass=true all buffers,flows, and operations downstream of campaignable buffers 314 a-314 c.The actual Finished Goods Demands are also marked as lp_pass=true sothat it is considered for planning in LPOPT. The WIP/fixed flows whichare defined downstream of campaignable buffers 314 a-314 c are alsomarked as LP pass true.

At activity 528 of stage three 606, solver 200 reverts the bucketstructure to daily, weekly, and monthly buckets. Additionally, the timebuckets are restored to the original requirements of the supply chainplanning problem, and dummy demands and dummy delivery operationscreated during stage two 604 for campaignable buffers 314 a-314 c areremoved. Further, the producing operations on campaignable buffers 314a-314 c in the campaignable period which are not part of the output ofstage two 604 are switched off so that campaign planning during stagethree 606 does not plan anything on these operations.

At activity 530 of stage three 606, solver 200 formulates the supplychain problem and solves the LPOPT objective followed by lotsizingwithout campaign planning. Further, campaign-specific objective levels410 are removed and replaced with the original non-campaign objectivelevels 412. Because the stage three 606 planning problem begins with asolved campaign plan, solver 200 may satisfy demands only by addingarticles to an existing campaign, not by adding a new campaign ordeleting an existing campaign. At this point, the LPOPT option file willnow have all the LPOPT options for regular lotsizing options.

At activity 532 of stage three 606, solver 200 generates the output fromstate three 606 comprising a supply chain plan and combines it with theoutput of stage two 604 comprising a campaign plan to generate anoverall optimized supply chain plan including a campaign plan.

After stage three 606 ends at activity 532, method 500 may continue toactivity 534, where solver 200 uses the supply chain plan and campaignplan generated by segmented campaign planning to instruct automatedmachinery (i.e., robotic warehouse systems, robotic inventory systems,automated guided vehicles, mobile racking units, automated roboticproduction machinery, robotic devices and the like) to adjust productmix ratios, inventory levels at various stocking points, production ofproducts of manufacturing equipment, proportional or alternativesourcing of one or more supply chain entities, and the configuration andquantity of packaging and shipping of items based on a supply chainplan, including a supply chain master plan and/or a campaign plan, thenumber of items currently in the inventory one or more supply chainentities 150, the number of items currently in transit in thetransportation network 140, forecasted demand, a supply chaindisruption, and/or one or more other factors described herein

It should be noted that the item and process described herein is asimplified description for the purpose of illustration. For example, theitems may be different sizes, styles, states of same or differentphysical material. Similarly, a process may be any process or operation,including manufacturing, distribution, transportation, or any othersuitable action of supply chain network 100. In one embodiment,additional constraints, such as, for example, business constraints,operation constraints, and resource constraints, may be added tofacilitate other planning rules. The business objectives are prioritizedand modeled as hierarchy of objective functions. This model is a linearprogramming (LP) problem which does not consider discrete variables orconstraints. For the sake of simplicity, and without loss of generality,all the objective functions are assumed to be minimized. If anyobjective function is to be maximized, the objective function isnegatived and minimized.

As explained above, the segmented campaign planning process comprisesmodifying supply chain models, objectives, and buckets to create lesscomplex campaign planning problems, which may be solved more quicklyusing less computing resources than a single segment campaign planningproblem, while still generating plans of equivalent or satisfactoryquality. By segmenting the campaign planning process as described above,this change, the runtime of the workflow of campaign planning wasdrastically reduced (e.g. from 120 hours to 25 hours), and all thecampaign planning constraints and business objectives were respectedwhich ensured better overall plan quality. In contrast, a single stagecampaign planning problem is infeasible for large datasets because thehigh complexity of campaign planning problems, which means that, evenwith a large amount of computer resources, the problem may take over 120hours to solve, which limits the usefulness of the solution, since itmay already be stale by the time it is generated.

FIG. 7 illustrates a comparison of results for single stage campaignplanning and segmented campaign planning for a small production dataset.A small dataset may comprise a portion of a supply chain networkrequired to produce one or two finished goods and/or one or twomanufacturing plants. Chart 700 illustrates for each of the solvedobjective levels, the segmented stage process generated a supply chainmaster plan with results the same or equivalent to that produced by thesingle stage process, including satisfying the same amount of demand,having the same lateness for backlogged items, and a build ahead andmanufacturing quantity with equivalent results. This indicates for smalldatasets segmented campaign planning produces high-quality supply chainmaster plans. In addition to generating high-quality plan for a smalldataset, segmented campaign planning also generates high-quality plansfor large datasets.

FIG. 8 illustrates a comparison of results for single stage campaignplanning and segmented campaign planning for a large production dataset.A large production dataset may comprise, for example, a supply chainproblem comprising a larger portion (or the entirety) of a supply chainnetwork 100. For example, the large production dataset used in the abovecomparison comprises approximately 6000 finished goods, nineteenmanufacturing plants, a global supply chain with global demands, and aworldwide distribution network. As illustrated, in one-year supply chainmaster planning using the segmented campaign planning process, thedemand satisfaction was similar to the result from supply chain masterplanning using a single campaign planning problem, even though thesegmented campaign planning used much less memory and took approximately118 hours less to complete. Because segmented campaign planning can beaccomplished in one or two days, this allows campaign planning to be runon weekends as part of a master planning solution, which means thecampaign plan may be distributed weekly to individual factories forimplementation.

Chart 800 comprises a comparison of demand fulfillment sorted accordingto demand layers. The total demand for the exemplary large productiondataset comprises 174,000 items, prioritized by fourteen differentlayers. Each layer represents demand having a particular priority,sorted from a highest priority demand at Layer0 descending to a lowestpriority demand at layer 13. During LP solving, Solver 200 may solveeach layer one at a time, hierarchically, before moving to a lowerpriority layer. According to embodiments, demands are prioritized basedon the importance of the customer and the time value of the finishedgoods. A highest priority demand might comprise, for example, a highvalue good requested by a high priority customer which if notimmediately fulfilled will be lated. According to embodiments, Solver200 may solve the supply chain master planning problem to try to firstmeet the highest priority demand (demand for Layer0) before solving thesupply chain master planning problem to satisfy the next lower prioritydemand (demand for Layer1). In this manner, solver 200 may iterativelysolve supply chain master planning problem for the demand of each layer,one layer at a time, in order of priority, until material or capacity isexhausted, or all demand layers are processed. Although the exemplarylarge production dataset comprises three levels of customer importanceand three values of finished goods, embodiments contemplate any numberof priority levels associated with demands, goods, customers, brandvalue, customer segmentation, priority of finished good, priority ofcustomer demand, and the like, according to particular needs.

Reference in the foregoing specification to “one embodiment”, “anembodiment”, or “some embodiments” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the invention. The appearancesof the phrase “in one embodiment” in various places in the specificationare not necessarily all referring to the same embodiment.

While the exemplary embodiments have been shown and described, it willbe understood that various changes and modifications to the foregoingembodiments may become apparent to those skilled in the art withoutdeparting from the spirit and scope of the present invention.

What is claimed is:
 1. A system, comprising: a supply chain networkcomprising a robotic inventory system, wherein the robotic inventorysystem is configured to adjust inventory levels; a networkinterconnecting a planner and the robotic inventory system; the planner,further comprising a processor and a non-transitory computer-readablestorage medium, the planner configured to: model the supply chainnetwork over a planning horizon, the planning horizon comprising one ormore time buckets; formulate a supply chain master planning problemcomprising a hierarchy of objective functions and one or moreconstraints, wherein each objective function of the hierarchy ofobjective functions is expressed as a minimization; identify one or morecampaignable buffers and one or more campaignable resources; divide thesupply chain master planning problem into a first segment, a secondsegment comprising the one or more campaignable buffers and the one ormore campaignable resources, and a third segment comprising one or moreitems and operations downstream of the one or more campaignableresources and the one or more campaignable buffers; modeling one or morecampaign objective functions specific to a campaign planning solution;segment a campaign planning problem into three stages; solve a campaignplanning problem of a first stage of the three stages to determineprioritized production demands on the one or more campaignable buffers;solve a campaign planning problem of a second stage of the three stagesto determine a timing and a sequence for allocating the one or morecampaignable resources; solve a campaign planning problem of a thirdstage of the three stages to determine a quantity of one or moreproducts to produce on at least one of the one or more campaignableresources by solving the campaign planning problem of the second stage;and instruct the robotic inventory system to adjust inventory levels atvarious stocking points determined by the solution of the campaignplanning problem of the third stage.
 2. The system of claim 1, whereineach stage of the three stages comprises a supply chain problem having adifferent combination of time-bucket granularity, supply chain staticstructure and objectives.
 3. The system of claim 1, wherein one or morematerial flows in the supply chain network are further modelled asbidirectional material flows.
 4. The system of claim 1, wherein the oneor more campaignable resources each comprise a resource requiring anamount of time to change from production of a first item to productionof a second item.
 5. The system of claim 1, wherein the prioritizedproduction demands are prioritized based on an importance of a customerand a time value of the one or more products.
 6. The system of claim 1,wherein the supply chain network is further modelled as a network of oneor more nodes, one or more edges and one or more buffers.
 7. The systemof claim 1, wherein the prioritized production demands are prioritizedinto priority levels based on one or more of: demands, goods, customers,brand value, customer segmentation, priority of finished good andpriority of customer demand.
 8. A method, comprising: providing a supplychain network comprising a robotic inventory system, wherein the roboticinventory system is configured to adjust inventory levels;interconnecting via a network a planner and the robotic inventorysystem; modeling, by the planner comprising a processor and anon-transitory computer-readable storage medium, the supply chainnetwork over a planning horizon, the planning horizon comprising one ormore time buckets; formulating, by the planner, a supply chain masterplanning problem comprising a hierarchy of objective functions and oneor more constraints, wherein each objective function of the hierarchy ofobjective functions is expressed as a minimization; identifying, by theplanner, one or more campaignable buffers and one or more campaignableresources; dividing, by the planner, the supply chain master planningproblem into a first segment, a second segment comprising the one ormore campaignable buffers and the one or more campaignable resources,and a third segment comprising one or more items and operationsdownstream of the one or more campaignable resources and the one or morecampaignable buffers; modeling, by the planner, one or more campaignobjective functions specific to a campaign planning solution;segmenting, by the planner, a campaign planning problem into threestages; solving, by the planner, a campaign planning problem of a firststage of the three stages to determine prioritized production demands onthe one or more campaignable buffers; solving, by the planner, acampaign planning problem of a second stage of the three stages todetermine a timing and a sequence for allocating the one or morecampaignable resources; solving, by the planner, a campaign planningproblem of a third stage of the three stages to determine a quantity ofone or more products to produce on at least one of the one or morecampaignable resources by solving the campaign planning problem of thesecond stage; and instructing, by the planner, the robotic inventorysystem to adjust inventory levels at various stocking points determinedby the solution of the campaign planning problem of the third stage. 9.The method of claim 8, wherein each stage of the three stages comprisesa supply chain problem having a different combination of time-bucketgranularity, supply chain static structure and objectives.
 10. Themethod of claim 8, wherein one or more material flows in the supplychain network are further modelled as bidirectional material flows. 11.The method of claim 8, wherein the one or more campaignable resourceseach comprise a resource requiring an amount of time to change fromproduction of a first item to production of a second item.
 12. Themethod of claim 8, wherein the prioritized production demands areprioritized based on an importance of a customer and a time value of theone or more products.
 13. The method of claim 8, wherein the supplychain network is further modelled as a network of one or more nodes, oneor more edges and one or more buffers.
 14. The method of claim 8,wherein the prioritized production demands are prioritized into prioritylevels based on one or more of: demands, goods, customers, brand value,customer segmentation, priority of finished good and priority ofcustomer demand.
 15. A non-transitory computer-readable medium embodiedwith software, the software when executed configured to: interconnectvia a network, a supply chain network comprising a robotic inventorysystem and a planner, wherein the robotic inventory system is configuredto adjust inventory levels and the planner comprises a processor andmemory; model the supply chain network over a planning horizon, theplanning horizon comprising one or more time buckets; formulate a supplychain master planning problem comprising a hierarchy of objectivefunctions and one or more constraints, wherein each objective functionof the hierarchy of objective functions is expressed as a minimization;identify one or more campaignable buffers and one or more campaignableresources; divide the supply chain master planning problem into a firstsegment, a second segment comprising the one or more campaignablebuffers and the one or more campaignable resources, and a third segmentcomprising one or more items and operations downstream of the one ormore campaignable resources and the one or more campaignable buffers;model one or more campaign objective functions specific to a campaignplanning solution; segment a campaign planning problem into threestages; solve a campaign planning problem of a first stage of the threestages to determine prioritized production demands on the one or morecampaignable buffers; solve a campaign planning problem of a secondstage of the three stages to determine a timing and a sequence forallocating the one or more campaignable resources; solve a campaignplanning problem of a third stage of the three stages to determine aquantity of one or more products to produce on at least one of the oneor more campaignable resources by solving the campaign planning problemof the second stage; and instruct the robotic inventory system to adjustinventory levels at various stocking points determined by the solutionof the campaign planning problem of the third stage.
 16. Thenon-transitory computer-readable medium of claim 15, wherein each stageof the three stages comprises a supply chain problem having a differentcombination of time-bucket granularity, supply chain static structureand objectives.
 17. The non-transitory computer-readable medium of claim15, wherein one or more material flows in the supply chain network arefurther modelled as bidirectional material flows.
 18. The non-transitorycomputer-readable medium of claim 15, wherein the one or morecampaignable resources each comprise a resource requiring an amount oftime to change from production of a first item to production of a seconditem.
 19. The non-transitory computer-readable medium of claim 15,wherein the prioritized production demands are prioritized based on animportance of a customer and a time value of the one or more products.20. The non-transitory computer-readable medium of claim 15, wherein thesupply chain network is further modelled as a network of one or morenodes, one or more edges and one or more buffers.